{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/bkkaggle/pytorch-CycleGAN-and-pix2pix/blob/master/CycleGAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5VIGyIus8Vr7"
   },
   "source": [
    "Take a look at the [repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) for more information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7wNjDKdQy35h"
   },
   "source": [
    "# Install"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TRm-USlsHgEV"
   },
   "outputs": [],
   "source": [
    "!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Pt3igws3eiVp"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('pytorch-CycleGAN-and-pix2pix/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z1EySlOXwwoa"
   },
   "outputs": [],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8daqlgVhw29P"
   },
   "source": [
    "# Datasets\n",
    "\n",
    "Download one of the official datasets with:\n",
    "\n",
    "-   `bash ./datasets/download_cyclegan_dataset.sh [apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos]`\n",
    "\n",
    "Or use your own dataset by creating the appropriate folders and adding in the images.\n",
    "\n",
    "-   Create a dataset folder under `/dataset` for your dataset.\n",
    "-   Create subfolders `testA`, `testB`, `trainA`, and `trainB` under your dataset's folder. Place any images you want to transform from a to b (cat2dog) in the `testA` folder, images you want to transform from b to a (dog2cat) in the `testB` folder, and do the same for the `trainA` and `trainB` folders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vrdOettJxaCc"
   },
   "outputs": [],
   "source": [
    "!bash ./datasets/download_cyclegan_dataset.sh horse2zebra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gdUz4116xhpm"
   },
   "source": [
    "# Pretrained models\n",
    "\n",
    "Download one of the official pretrained models with:\n",
    "\n",
    "-   `bash ./scripts/download_cyclegan_model.sh [apple2orange, orange2apple, summer2winter_yosemite, winter2summer_yosemite, horse2zebra, zebra2horse, monet2photo, style_monet, style_cezanne, style_ukiyoe, style_vangogh, sat2map, map2sat, cityscapes_photo2label, cityscapes_label2photo, facades_photo2label, facades_label2photo, iphone2dslr_flower]`\n",
    "\n",
    "Or add your own pretrained model to `./checkpoints/{NAME}_pretrained/latest_net_G.pt`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B75UqtKhxznS"
   },
   "outputs": [],
   "source": [
    "!bash ./scripts/download_cyclegan_model.sh horse2zebra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yFw1kDQBx3LN"
   },
   "source": [
    "# Training\n",
    "\n",
    "-   `python train.py --dataroot ./datasets/horse2zebra --name horse2zebra --model cycle_gan`\n",
    "\n",
    "Change the `--dataroot` and `--name` to your own dataset's path and model's name. Use `--gpu_ids 0,1,..` to train on multiple GPUs and `--batch_size` to change the batch size. I've found that a batch size of 16 fits onto 4 V100s and can finish training an epoch in ~90s.\n",
    "\n",
    "Once your model has trained, copy over the last checkpoint to a format that the testing model can automatically detect:\n",
    "\n",
    "Use `cp ./checkpoints/horse2zebra/latest_net_G_A.pth ./checkpoints/horse2zebra/latest_net_G.pth` if you want to transform images from class A to class B and `cp ./checkpoints/horse2zebra/latest_net_G_B.pth ./checkpoints/horse2zebra/latest_net_G.pth` if you want to transform images from class B to class A.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0sp7TCT2x9dB"
   },
   "outputs": [],
   "source": [
    "!python train.py --dataroot ./datasets/horse2zebra --name horse2zebra --model cycle_gan --use_wandb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9UkcaFZiyASl"
   },
   "source": [
    "# Testing\n",
    "\n",
    "-   `python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout`\n",
    "\n",
    "Change the `--dataroot` and `--name` to be consistent with your trained model's configuration.\n",
    "\n",
    "> from https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix:\n",
    "> The option --model test is used for generating results of CycleGAN only for one side. This option will automatically set --dataset_mode single, which only loads the images from one set. On the contrary, using --model cycle_gan requires loading and generating results in both directions, which is sometimes unnecessary. The results will be saved at ./results/. Use --results_dir {directory_path_to_save_result} to specify the results directory.\n",
    "\n",
    "> For your own experiments, you might want to specify --netG, --norm, --no_dropout to match the generator architecture of the trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uCsKkEq0yGh0"
   },
   "outputs": [],
   "source": [
    "!python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout --use_wandb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OzSKIPUByfiN"
   },
   "source": [
    "# Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9Mgg8raPyizq"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = plt.imread('./results/horse2zebra_pretrained/test_latest/images/n02381460_1010_fake.png')\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0G3oVH9DyqLQ"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = plt.imread('./results/horse2zebra_pretrained/test_latest/images/n02381460_1010_real.png')\n",
    "plt.imshow(img)"
   ]
  }
 ],
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   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "CycleGAN",
   "provenance": []
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  "environment": {
   "name": "tf2-gpu.2-3.m74",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-3:m74"
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